Image quality evaluation system, method and program

ABSTRACT

Disclosed is a picture quality evaluation system that comprises a difference calculation part, which calculates the difference between data that represent a feature value of a pixel set comprising at least 1 pixel that constitutes a first image and data that represent a feature value of a pixel set comprising at least 1 pixel that constitutes a second image, a main area of focus calculation part, which uses at least the first image or the second image to determine the main area of focus of an image that has a specific feature and then calculates the main area of focus, which indicates the extent of the main area of focus, a difference weighting part, which weights the difference in the feature value in the pixel set contained in the main area of focus, based on the main area of focus, and a picture quality value calculation part, which calculates the picture quality value of the first image, based on the weighted difference from the difference weighting part.

TECHNICAL FIELD

The present invention relates to image quality evaluation system, methodand program.

BACKGROUND ART

Methods of objectively evaluating image quality of image datatransmitted via a network (which will be referred to as an imagehereinbelow) or coded images include methods of using the absolutedifference, squared difference, and S/N ratio of pixel values(luminances, color differences, RGB values, etc.) between an originalimage and an image of interest for evaluation. In the methods, however,a difference between two images is directly reflected on the objectiveimage quality value without taking account of human visual properties,and therefore, there has been a problem that the correlation with aresult of subjective evaluation made by a person via visual evaluationis low.

A method relating to the problem is disclosed in Patent Document 1. Theimage quality evaluation method as disclosed in Patent Document 1improves the correlation with the subjective evaluation value byapplying weighting to the absolute difference, squared difference, S/Nratio or the like as described above based on the power of thealternating-current (AC) components for pixel values while takingaccount of human visual properties varying with the spatial frequency.It is known that a human visual property is insensitive to signals ofhigher frequency, and weighting is controlled according to the amount ofsignals of higher frequency contained in an image.

Patent Document 1: JP-3458600B2

DISCLOSURE OF THE INVENTION Problems to be Solved by the Invention

The image quality evaluation method disclosed in Patent Document 1,however, poses a problem that it may still give a low correlation with aresult of subjective evaluation. For example, it is known that in aresult of subjective evaluation, quality degradation is detected moreeasily in a video area upon which a person is likely to focus (whichwill be referred to as a focused area hereinbelow) than in other videoareas. However, the method disclosed in Patent Document 1 does not takeaccount of whether an area is a focused area. Hence, a focused area maybe evaluated to be less degraded when the area contains a larger amountof high-frequency signals. On the other hand, areas other than thefocused area may be evaluated to be more degraded when the areas have asmaller amount of high-frequency signals.

Thus, the present invention has been made in view of such a problem, andits object is to provide image quality evaluation system, method andprogram with which a higher correlation with a result of subjectiveevaluation can be obtained.

Means for Solving the Problems

The present invention for solving the aforementioned problem is an imagequality evaluation system, characterized in comprising: a differencecalculating section for calculating a difference between datarepresenting a feature of a pixel group comprised of at least one pixelmaking up a first image, and data representing a feature of a pixelgroup comprised of at least one pixel making up a second image; adegree-of-focused-area calculating section for using at least one ofsaid first and second images to decide a focused area in the imagehaving a predetermined feature, and calculating a degree of focused areaindicating the degree of being said focused area; a difference weightingsection for applying weighting to the difference in feature for a pixelgroup falling within said focused area based on said degree of focusedarea; and an image quality value calculating section for calculating animage quality value for said first image based on the differenceweighted by said difference weighting section.

The present invention for solving the aforementioned problem is an imagequality evaluation method, characterized in comprising: calculating adifference between data representing a feature of a pixel groupcomprised of at least one pixel making up a first image, and datarepresenting a feature of a pixel group comprised of at least one pixelmaking up a second image; using at least one of said first and secondimages to decide a focused area in the image having a predeterminedfeature, and calculating a degree of focused area indicating the degreeof being said focused area; applying weighting to the difference infeature for a pixel group falling within said focused area based on saiddegree of focused area; and calculating an image quality value for saidfirst image based on said weighted difference.

The present invention for solving the aforementioned problem is aprogram, characterized in causing an information processing apparatus toexecute the processing of: calculating a difference between datarepresenting a feature of a pixel group comprised of at least one pixelmaking up a first image, and data representing a feature of a pixelgroup comprised of at least one pixel making up a second image; using atleast one of said first and second images to decide a focused area inthe image having a predetermined feature, and calculating a degree offocused area indicating the degree of being said focused area; applyingweighting to the difference in feature for a pixel group falling withinsaid focused area based on said degree of focused area; and calculatingan image quality value for said first image based on said weighteddifference.

EFFECTS OF THE INVENTION

The present invention provides a result of image quality evaluationhaving a higher correlation with a result of subjective evaluation.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram showing a configuration of a first embodimentof the present invention.

FIG. 2 is a flow chart for explaining an operation of the firstembodiment of the present invention.

FIG. 3 is a block diagram showing a configuration of a second embodimentof the present invention.

FIG. 4 is a block diagram showing a configuration of a third embodimentof the present invention.

FIG. 5 is a diagram for explaining a pixel area examined to detect afocused area in the third embodiment of the present invention.

FIG. 6 is a block diagram showing a computer system, which is a fourthembodiment of the present invention.

FIG. 7 is a flow chart for explaining an operation in an example of thepresent invention.

EXPLANATION OF SYMBOLS

-   -   101 Difference calculating section    -   102 Degree-of-focused-area calculating section    -   103 Difference weighting section    -   104 Image quality calculating section

BEST MODES FOR CARRYING OUT THE INVENTION First Embodiment

A first embodiment of the present invention will now be described indetail with reference to the accompanying drawings.

Referring to FIG. 1, an image evaluation system in the first embodimentof the present invention is comprised of a difference calculatingsection 101, a degree-of-focused-area calculating section 102, adifference weighting section 103, and an image quality calculatingsection 104.

The difference calculating section 101 is supplied as input with datarepresenting a feature of a first image, which is an image of interestfor evaluation, and data representing a feature of a second image, whichis an original image for use as a control for comparison, for each groupof pixels comprised of at least one pixel (which will be referred to asa pixel group hereinbelow). It should be noted that the first and secondimages may be moving pictures or still images.

In the present invention, a pixel group refers to a group of pixelscomprised of at least one pixel, and is a concept encompassing not onlya group of a plurality of pixels but also a group containing only onepixel. An example of a pixel group having a plurality of pixels may be ablock of 16 by 16 pixels.

An example of data representing a feature may be a pixel value of apixel when the pixel group contains one pixel. The pixel value refersto, for example, information on the luminance value, color differencevalue or RGB value, or a combination thereof.

Another example of data representing a feature may be an average ofpixel values of pixels within a pixel group when the pixel group is agroup of a plurality of pixels. Besides, the data representing a featuremay be a statistical quantity of AC components for a pixel group, whichmay be, for example, an average of absolute differences obtained bycalculating an average of pixel values of pixels within a pixel group,and calculating an absolute difference between that average and a pixelvalue of each pixel within the pixel group to determine the average ofthe absolute differences, or may be a variance of pixel values withinthe pixel group. Moreover, the data representing a feature may be atransform factor after applying orthogonal transform to pixel values ina pixel group. While the feature corresponding to a pixel group may beinput to the difference calculating section 101 as described above whenthe pixel group is a group of a plurality of pixels, a configuration inwhich pixel values of pixels in a pixel group are input to thedifference calculating section 101 and a feature is calculated on apixel group-by-pixel group basis as described above at the differencecalculating section 101 may be contemplated.

The difference calculating section 101 calculates a differential valuein feature between the data representing the feature for the first imageand that representing the feature for the second image. For example, ina case that the first and second images are moving picture data, datarepresenting a feature input to the difference calculating section 101is a feature of a pixel group in each frame in the moving picture. Thedifferential value is calculated as an absolute value of a difference ora squared difference between, for example, a feature of a pixel group ata position in a frame in the first image and that of a pixel group atthe same position and in a frame at the same time as those of the firstimage, in the second image.

It should be noted that the data representing a feature for the firstimage or that representing a feature for the second image is not alwaysthe feature of all pixel groups within the image. For example, whenimage transmission in a network is involved, values for only part ofpixel groups within an image can be acquired due to transmission errorsor the like in some cases. In other cases, to reduce the transmissionload, the feature of only part of pixel groups within an image istransmitted in the first place, for example, in a case that datarepresenting a feature for the second image is transmitted for everyother pixel group. In such cases, a differential value is calculated forthe feature of a pixel group only in the frame and at the position thatcan be referred to by both the first and second images.

The degree-of-focused-area calculating section 102 is supplied withpixel values of at least part of pixels in the first image as input.Although not shown in FIG. 1, pixel values of at least part of pixels inthe second image may be supplied as input. Moreover, pixel values of atleast part of pixels in the first and second images may be supplied asinput, as described earlier. In this case, respective degrees of focusedarea, which will be discussed later, are calculated for the first andsecond images, and the average thereof may be regarded as the degree offocused area for the current pixel.

The degree-of-focused-area calculating section 102 identifies a focusedarea that has a predetermined feature within an image, and calculates adegree of focused area indicating the degree of being a focused area.

As used herein, the predetermined feature refers to, for example, aspecific color or a specific shape (for example, characters, a face, ora specific building). The focused area is an area having such a featurein an image.

In identifying a focused area, first, a pixel value is looked up on apixel-by-pixel basis, and a degree of pixel of interest indicating adegree of how much the current pixel is a pixel of interest having thepredetermined feature is calculated. As an example, a pixel having acolor that resembles a human skin is defined as pixel of interest. Inparticular, decision is made as to whether an input pixel is a pixel ofinterest depending upon whether the pixel value of the pixel fallswithin a specific range in a color space. Examples of the color spacesinclude a YCbCr color space that is represented by luminance value andcolor difference values, and an RGB color space represented by RGBvalues indicating three primary colors, red, blue and green. It shouldbe noted that definition of a pixel of interest is not limited to acolor that resembles a human skin, and may be another definition. Forexample, in a case that the YCbCr color space is employed, values of theluminance Y, blue color difference Cb, and red color difference Cr foreach pixel are input as a pixel value to the degree-of-focused-areacalculating section 102.

Moreover, in a case that the pixel value of an input pixel has aspecific color, the current pixel of interest is decided to be a pixelof interest. In general, a person tends to focus upon a human figureappearing in an image, so that a color that resembles a human skin coloris defined as the specific color. In particular, the specific color isdefined as such a range in a YCbCr color space that covers a whole rangein an RGB space containing a color recognized to resemble a skin colorvia subjective experimentation (visual evaluation) as represented in theRGB color space. Therefore, a range broader than the skin color in RGBrepresentation satisfies the definition. In a case that the luminance Yranges 48≦y≦224, blue difference Cb ranges 104<Cb<125, and reddifference Cr ranges 135<Cr<171 according to subjective experimentationas described above, it is considered that the color resembles a humanskin color and that pixel is decided to be a pixel of interest. Therange of pixel values is not limited to the range of values describedabove, and may include other values.

Next, based on a result of the decision as to whether each pixel is apixel of interest, the degree of pixel of interest for the pixel that isa pixel of interest is defined as one, and that for the pixel that isnot a pixel of interest is defined as zero. It should be noted that thedegree of pixel of interest is not limited to only zero and one, whichis based upon whether a current pixel is a pixel of interest or not, andit may be a continuous or multi-step value. For example, a method ofdefining a value obtained by normalizing the distance from a specificpoint in the YCbCr color space as the degree of pixel of interest may becontemplated.

Next, a focused area is identified based on the calculated degree ofpixel of interest, and a degree of focused area for the focused area iscalculated. A method of calculating the degree of focused area involvesdefining a set of pixels having a degree of pixel of interest of one asa focused area, defining a degree of focused area for pixels within thearea as one, and defining a degree of focused area for pixels outsidethe area as zero. It should be noted that in a case that the degree ofpixel of interest is defined as a continuous or multi-step value, themethod may involve comparing the degree of pixel of interest with apredefined threshold, defining a set of pixels having a degree of pixelof interest higher than the predefined threshold as a focused area, anddefining the degree of focused area of pixels within the focused area asone.

The degree of focused area may also be a continuous or multi-step value,and for example, in a case that the degree of pixel of interest isdefined as a continuous or multi-step value, and a set of pixels havinga higher degree of pixel of interest than a predefined threshold isdefined as a focused area, the degree of focused area may be defined asa continuous or multi-step value in proportion with the total or averageof the degree of pixel of interest for pixels contained in the focusedarea. Moreover, a pixel having a degree of focused area of zero near apixel having a degree of focused area of non-zero may be redefined tohave a degree of focused area of non-zero. This is done by takingaccount of the fact that while a person watches a focused area, pixelsnear the area come into sight.

The difference weighting section 103 uses the degree of focused areacalculated at the degree-of-focused-area calculating section 102 toapply weighting to the differential value for a pixel group fallingwithin the focused area, and outputs a weighted differential value.

In applying weighting to the differential value for a pixel group, adifferential value for a pixel group containing pixels having a degreeof focused area of one is multiplied by A, and that for a pixel groupcontaining pixels having a degree of focused area of zero is multipliedby B. A, B are weighting factors, where A>B. For example, processing isexecuted such that a differential value for a pixel group containingpixels having a degree of focused area of one is multiplied by two, andthat for a pixel group containing pixels having a degree of focused areaof zero is multiplied by one. As another example, in a case that thedegree of focused area for each pixel is calculated as a continuousvalue, the weighting factor may be defined such that (weightingfactor)=1+(degree of focused area), or the like.

In a case that a pixel group is comprised of a plurality of pixels, partof the pixel group may not fall within a focused area. In such a case, adecision is made as to whether a pixel group falls within a focused areaaccording to a proportion of the area of the pixel group falling withinthe focused area, and in a case that the pixel group is regarded aswithin the focused area, those pixels falling outside the focused areaare also regarded as within the focused area and weighting is appliedthereto using the weighting factor similar to that described above.

The image quality value calculating section 104 outputs an image qualityvalue for evaluating image quality of the first image based on thedifferential value weighted by the difference weighting section 103. Theimage quality value is calculated in the form of, for example, anaverage of weighted differential values of the whole first image. Theimage quality value to be output may be output directly as an average,or converted into another form such as the S/N ratio and then output.

Next, an operation in this embodiment will be described with referenceto FIG. 2.

At Step 11, a difference between data representing a feature for a firstimage and data representing a feature for a second image is calculated.

At Step 21, a focused area is identified, and a degree of focused areafor a pixel in the focused area is calculated.

At Step 31, the degree of focused area calculated at Step 21 is used toapply weighting to the difference calculated at Step 11 for each pixelgroup described above.

At Step 41, an image quality value for the first image is calculatedbased on the difference weighted at Step 31 to evaluate the first image.

It should be noted that Steps 11 and 21 may be run in a temporallyreverse order, or in parallel.

The first embodiment can produce a result of image quality evaluationhaving a higher correlation with a result of subjective evaluation. Thereason of this is that image quality evaluation is made while takingaccount of whether an area is a focused area that is focused by a personby calculating the degree of focused area. Especially, by defining acolor resembling a human skin as the focused area, the degree of focusedarea can be calculated to have a high correlation with a degree ofactual focusing by a person. This is because a person generally tends tofocus upon a human figure in a video, if present.

Second Embodiment

A second embodiment of the present invention will now be described indetail with reference to the accompanying drawings.

The second embodiment will address a case in which a pixel group iscomprised of one pixel.

Referring to FIG. 3, an image evaluation system in the second embodimentof the present invention is comprised of a difference calculatingsection 201, a degree-of-focused-area calculating section 202, adifference weighting section 203, and an image quality value calculatingsection 204.

The difference calculating section 201 is supplied as input with datarepresenting a feature of a first image, which is an image of interestfor evaluation, and data representing a feature of a second image, whichis an original image for use as a control for comparison, on apixel-by-pixel basis. An example of data representing a feature is apixel value of a pixel when the pixel group contains one pixel. Thepixel value refers to, for example, information on the luminance value,color difference value or RGB value, or a combination thereof.

The difference calculating section 201 calculates a differential valuebetween the data representing a feature for the first image and thatrepresenting a feature for the second image. For example, in a case thatthe first and second images are moving picture data, data representing afeature input to the difference calculating section 101 is a pixel valueof each pixel in each frame in the moving picture. In this case, thedifferential value is calculated as an absolute value of a difference ora squared difference between a pixel value of a pixel at a position in aframe in the first image and that of a pixel at the same position and ina frame at the same time as those of the first image, in the secondimage.

It should be noted that the data representing a feature for the firstimage or that representing a feature for the second image is not alwaysthe pixel value of all pixels within the image. For example, when imagetransmission in a network is involved, values for only part of pixelgroups within an image can be acquired due to transmission errors or thelike in some cases. In other cases, to reduce the transmission load,only part of pixel values within an image is transmitted in the firstplace, for example, in a case that data representing a feature for thesecond image is transmitted for every other pixel. In such cases, adifferential value is calculated only in the frame and at the positionthat can be referred to by both the first and second images.

The degree-of-focused-area calculating section 202 calculates a degreeof focused area indicating the degree of being a focused area. Since inthis embodiment, a pixel group contains one pixel, the followingdescription will be made considering the degree of pixel of interest inthe first embodiment described above directly as the degree of focusedarea.

The degree-of-focused-area calculating section 202 first decides whethera pixel is a pixel of interest for a focused area from the input pixelvalue.

In this embodiment, as an example, a pixel having a color that resemblesa human skin is defined as pixel of interest. In particular, decision ismade as to whether an input pixel is a pixel of interest depending uponwhether the pixel value of the pixel falls within a specific range in acolor space. Examples of the color spaces include a YCbCr color spacethat is represented by luminance value and color difference values, andan RGB color space represented by RGB values indicating three primarycolors, red, blue and green. It should be noted that definition of apixel of interest is not limited to a color that resembles a human skin,and may be another definition. For example, in a case that the YCbCrcolor space is employed, values of the luminance Y, blue colordifference Cb, and red color difference Cr for each pixel are input as apixel value to the degree-of-focused-area calculating section 102.

Moreover, in a case that the pixel value of an input pixel has aspecific color, the current pixel of interest is decided to be a focusedarea. In general, a person tends to focus upon a human figure appearingin a video, so that a color that resembles a human skin color is definedas the specific color. In particular, the specific color is defined assuch a range in a YCbCr color space that covers a whole range in an RGBspace containing a color recognized to resemble a skin color viasubjective experimentation (visual evaluation) as represented in the RGBcolor space. Therefore, a range broader than the skin color in RGBrepresentation satisfies the definition. In a case that the luminance Yranges 48≦y≦224, blue difference Cb ranges 104<Cb<125, and reddifference Cr ranges 135<Cr<171 according to subjective experimentationas described above, it is considered that the color resembles a humanskin color and that pixel is decided to be a pixel of interest. Therange of pixel values is not limited to the range of values describedabove, and may include other values.

Next, based on a result of the decision as to whether each pixel is apixel of interest, the degree of focused area for the pixel iscalculated and output. For example, the degree of focused area for thepixel that is a pixel of interest is defined as one, and that for thepixel that is not a pixel of interest is defined as zero. It should benoted that the degree of focused area may be a continuous or multi-stepvalue, instead of zero and one as described above. Besides, the degreeof focused area for a pixel lying near the pixel of interest may bedefined as 0.5. This is done by taking account of the fact that while aperson watches a focused area, pixels near the pixel come into sight.

Moreover, the decision of a pixel of interest is not limited to thatrelying upon the aforementioned method, and may rely upon another methodby, for example, detecting a viewer's line of sight.

The difference weighting section 203 uses the degree of focused areacalculated at the degree-of-focused-area calculating section 202 toapply weighting to the differential value calculated at the differencecalculating section 101 on a pixel-by-pixel basis, and outputs aweighted differential value.

Now an example of weighting when the degree of focused area iscalculated to have a value of 0, 1 for each pixel at thedegree-of-focused-area calculating section 202 will be described. Adifferential value of a pixel calculated to have a degree of focusedarea of one is multiplied by A, and that of a pixel calculated to have adegree of focused area of zero is multiplied by B. A, B are weightingfactors, where A>B. For example, processing is executed such that adifferential value of a pixel calculated to have a degree of focusedarea of one is multiplied by two, and that of a pixel calculated to havea degree of focused area of one is multiplied by one.

As another example, in a case that the degree of focused area for eachpixel is calculated as a continuous value, the weighting factor may bedefined such that (weighting factor)=1+(degree of focused area).

The image quality value calculating section 204 outputs an image qualityvalue for evaluating image quality of the first image based on thedifferential value weighted by the difference weighting section 203. Theimage quality value is calculated in the form of, for example, anaverage of weighted differential values for the whole first image. Theimage quality evaluation value to be output may be output directly as anaverage, or converted into another form such as the S/N ratio and thenoutput.

Third Embodiment

Next, a third embodiment of the present invention will now be describedin detail.

Referring to FIG. 4, an image evaluation system in the third embodimentof the present invention is comprised of a difference calculatingsection 301, a degree-of-focused-area calculating section 302, adifference weighting section 303, and an image quality value calculatingsection 304.

The third embodiment is similar to the first embodiment except that incalculating the degree of focused area for each pixel group at thedegree-of-focused-area calculating section 302, the pixel group iscomprised of not one pixel but a group of a plurality of pixels and thefocused area is identified on a pixel group-by-pixel group(block-by-block) basis. This embodiment addresses a case in which thedifference calculating section 301 is also supplied with datarepresenting a feature for the first image and that representing afeature for the second image for each pixel group that is the same asthe pixel group described above. Other components are similar to thosein the first embodiment, and accordingly, explanation thereof will beomitted.

The difference calculating section 301 is supplied as input with thedata representing a feature for the first image and that representing afeature for the second image. For example, in a case that an average ofpixel values within a pixel group is input, an absolute value of adifference between the average of pixel values within the pixel group ata position in a frame in the first image and that within the pixel groupat the same position and in a frame at the same time as those of thefirst image, in the second image is calculated as the differentialvalue.

The degree-of-focused-area calculating section 302 is supplied withpixel values of at least part of pixels in the first image, as in thefirst embodiment, and decides whether each input pixel is a pixel ofinterest. The method of decision is similar to that in the firstembodiment, and accordingly, explanation thereof will be omitted.

Next, based on a result of the decision as to whether each pixel is apixel of interest, a degree of focused area is calculated and output foreach pixel group, which is a group of a plurality of pixels.

As an example, a method of calculating a degree of focused area when apixel group is a block of 16 by 16 pixels will be described. First, thenumber of pixels decided to be a pixel of interest is accumulated withinthe pixel group. Then, a calculation is made such that in a case thatthe number of pixels is equal to or greater than a predefined threshold(for example, 128), the degree of focused area for the pixel group isdefined as one; otherwise, as zero. Besides, the degree of focused areamay be defined such that (degree of focused area)=(the number of pixelsdecided to be a pixel of interest)/(the number of pixels within thepixel group). For example, in a case that the number of pixels decidedto be a pixel of interest is thirty, the degree of focused area for thatpixel group is 30/(16×16).

In addition to accumulating the number of pixels decided to be a pixelof interest within a pixel group of interest for decision, the number ofpixels decided to be a pixel of interest may be accumulated in an extentalso including pixel groups near the pixel group. This is done by takingaccount of the fact that while a person watches an image, pixel groupsnear the pixel group come into sight. Moreover, from a similar reason,pixel groups having a degree of focused area of zero lying near a pixelgroup having a degree of focused area of non-zero may be modified tohave a degree of focused area of non-zero.

As an example, a case in which a pixel group is a block of 16 by 16pixels and a range shown in FIG. 5 represents vicinal pixel groups willnow be described.

In calculating a degree of focused area for a pixel group of interestfor decision, the number of pixels decided to be a pixel of interest isaccumulated in an extent including the pixel group of interest fordecision and vicinal pixel groups together (a block of 48 by 48 pixels).In a case that the number of pixels decided to be a pixel of interest is55, the degree of focused area for the pixel group of interest fordecision is 55/(48×48). It should be noted that the method ofcalculating a degree of focused area is not limited to that describedabove, and various methods may be contemplated, including a method ofapplying weighting depending upon whether a pixel group is a pixel groupof interest for decision or a vicinal pixel group.

In this embodiment, a value having a higher correlation with the degreeof actual focusing by a person can be calculated by identifying afocused area for each pixel group that is a group of a plurality ofpixels, and calculating a degree of focused area for the image group.This is because pixels of interest covering an extent having a certainsize are focused more than only one pixel is. For example, a persontends to focus upon an extent of a certain size (such as a whole face)of pixels having a pixel value that resembles a human skin color, ratherthan upon one pixel having a pixel value that resembles a human skincolor.

In defining a pixel group, a mode in which a component such as an objectrecognizing section is provided to apply object recognition to an imagebeforehand and define each object as a pixel group may be contemplated.By calculating a degree of focused area on an object-by-object basis, itis possible to calculate a degree of focused area more accurately.

Fourth Embodiment

Next, a fourth embodiment in the present invention will be describedwith reference to FIG. 6. In the fourth embodiment, the moving pictureprocessing apparatus described earlier regarding the first embodiment isimplemented by a computer system.

Referring to FIG. 6, the present system is provided with aprogram-controlled processor 401. The program-controlled processor 401is connected with a first image data buffer 402 and a second image databuffer 403, as well as a program memory 404 for storing therein requiredprograms. Program modules stored in the program memory 404 comprise amain program 405, as well as those for a difference calculationprocessing module 406, a degree-of-focused-area calculation processingmodule 407, a difference weighting processing module 408, and an imagequality calculation processing module 409. The main program 405 is aprincipal program for executing the image quality evaluation processing.The program modules for the difference calculating module 406,degree-of-focused-area calculation processing module 407, differenceweighting processing module 408, and image quality calculationprocessing module 409 are processing modules for implementing thefunctions of the difference calculating section 101,degree-of-focused-area calculating section 102, difference weightingsection 103, and image quality calculating section 104 described above,respectively.

Example 1

An example of the present invention will now be described.

The present invention is a specific example of the first embodiment.

In this example, the second image, which is an original image, is amoving picture of an SDTV size (720 pixels in a horizontal direction,480 pixels in a vertical direction, and 29.97 frames per second). Thefirst image, which is an image of interest for evaluation, is a movingpicture obtained by encoding the moving picture in an MPEG-2 format at 4Mbps and decoding the encoded image.

FIG. 7 is a flow chart illustrating an operation of this example.

At Step 201, one frame of image data for a second image is input to thedifference calculating section 201. At Step 202, one frame of image datafor a first image at the same time as that of the frame of image datafor the second image is input to the difference calculating section 201.At Step 203, the difference calculating section 201 extracts pixelvalues of the image data for these images at the same position, andcalculates an absolute difference between the pixel values as adifferential value. Assuming that the luminance value Y is 50 at acertain position in the second image and 52 in the first image, thedifferential value is 2.

At Step 204, the degree-of-focused-area calculating section 202 decideswhether the current pixel is a pixel of interest from the pixel valuefor the first image extracted at Step 203. In a case that it is a pixelof interest, the degree of focused area for that pixel is defined asone; otherwise, as zero. In particular, in a case that the pixel valuefalls within a range of a luminance Y ranging 48≦y≦224, a bluedifference Cb ranging 104<Cb<125, and a red difference Cr ranging135Cr<171, the pixel value is decided to be a pixel of interest. Forexample, in a case that the first image has a luminance value Y of 52, ablue difference Cb of 110, and a red difference Cr of 150, the valuesfall within the range described above, so that the pixel is decided tobe a pixel of interest and is given a degree of focused area of one.

In a case that the degree of focused area is one for the pixel, thedifference weighting section 103 multiplies the differential valuecorresponding to the pixel by two, and defines the resulting value as aweighted differential value at Step 205. In a case that the degree offocused area is zero for the pixel, the difference weighting section 103multiplies the differential value by one, and defines the resultingvalue as a weighted differential value at Step 206.

At Step 207, to determine a total value of weighted differential valuesfor the whole image, the weighted differential value is added to avariable Sum. The initial value of the variable Sum is zero.

At Step 208, a check is made as to whether the difference calculation iscompleted for all pixels in one frame. In a case that the calculation isnot completed, the process goes back to Step 203 and similar processingis applied to a pixel for which the difference calculation is notcompleted yet.

In a case that the calculation is completed, a check is made as towhether the processing is completed for all frames in the first image atStep 209. In a case that the processing is not completed, the processgoes back to Step 201 and similar processing is applied to a subsequentframe. In a case that the processing is completed, the image qualitycalculating section 104 outputs the total value Sum of the weighteddifferential values for the whole image as an image quality value atStep 210. By this operation, the processing is terminated.

The 1st mode of the present invention is characterized in that an imagequality evaluation system comprising: a difference calculating sectionfor calculating a difference between data representing a feature of apixel group comprised of at least one pixel making up a first image, anddata representing a feature of a pixel group comprised of at least onepixel making up a second image; a degree-of-focused-area calculatingsection for deciding a focused area in the image having a predeterminedfeature using at least one of said first and second images, andcalculating a degree of focused area indicating the degree of being saidfocused area; a difference weighting section for applying weighting tothe difference in feature for a pixel group falling within said focusedarea based on said degree of focused area; and an image quality valuecalculating section for calculating an image quality value for saidfirst image based on the difference weighted by said differenceweighting section.

The 2nd mode of the present invention, in the above-mentioned modes, ischaracterized in that said degree-of-focused-area calculating sectionperforms decision of a focused area on a pixel group-by-pixel groupbasis.

The 3rd mode of the present invention, in the above-mentioned modes, ischaracterized in that said degree-of-focused-area calculating sectiondecides whether a pixel is a pixel of interest for said focused areabased on at least a pixel value of said pixel making up said first orsecond image, and decides a focused area comprised of at least one ormore pixels based on said pixel of interest.

The 4th mode of the present invention, in the above-mentioned modes, ischaracterized in that said degree-of-focused-area calculating sectiondecides a pixel as a pixel of interest in a case that a pixel value ofsaid pixel falls within a specific range in a color space.

The 5th mode of the present invention, in the above-mentioned modes, ischaracterized in that said specific range in said color space is apredetermined range defined by a YCbCr color space represented by aluminance value and a color difference value.

The 6th mode of the present invention, in the above-mentioned modes, ischaracterized in that said specific range in said color space is a rangewith a value Y indicating the luminance ranging 48≦y≦224, a value Cbindicating the blue difference ranging 104<Cb<125, and a value Crindicating the red difference ranging 135<Cr<171.

The 7th mode of the present invention, in the above-mentioned modes, ischaracterized in that said specific range in said color space is apredetermined range defined in an RGB color space represented by RGBvalues indicating three primary colors, red, blue and green.

The 8th mode of the present invention, in the above-mentioned modes, ischaracterized in that said degree-of-focused-area calculating sectioncalculates a degree of focused area for a pixel group of pixels decidedto be said pixel of interest as one and that for a pixel group of pixelsdecided not to be said pixel of interest as zero.

The 9th mode of the present invention, in the above-mentioned modes, ischaracterized in that said degree-of-focused-area calculating sectionaccumulates the number of pixels decided to be said pixel of interestamong pixels within said pixel group, and calculates a degree of focusedarea for said pixel group based on a result of said accumulation.

The 10th mode of the present invention, in the above-mentioned modes, ischaracterized in that said degree-of-focused-area calculating sectionaccumulates the number of pixels decided to be a pixel of interest amongpixels within said pixel group and the number of pixels decided to be apixel of interest among pixels near said pixel group, and calculates adegree of focused area for said pixel group based on a result of theaccumulation.

The 11th mode of the present invention, in the above-mentioned modes, ischaracterized in that said data representing a feature for the firstimage and that representing a feature for the second image areinformation on any one of a luminance value, a color difference valueand an RGB value, or a combination thereof, for at least part of pixelsmaking up said first and second images.

The 12th mode of the present invention, in the above-mentioned modes, ischaracterized in that said data representing a feature for the firstimage and that representing a feature for the second image are anaverage of information on any one of a luminance value, a colordifference value and an RGB value, or a combination thereof, for pixelscontained in at least part of pixel groups making up said first andsecond images.

The 13th mode of the present invention, in the above-mentioned modes, ischaracterized in that said data representing a feature for the firstimage and that representing a feature for the second image are anaverage of absolute differences within an image group between an averageof information on any one of a luminance value, a color difference valueand an RGB value, or a combination thereof, and said information foreach pixel within said pixel group, for pixels contained in at leastpart of pixel groups making up said first and second images.

The 14th mode of the present invention, in the above-mentioned modes, ischaracterized in that said data representing a feature for the firstimage and that representing a feature for the second image are avariance of information on any one of a luminance value, a colordifference value and an RGB value, or a combination thereof, for pixelscontained in at least part of pixel groups making up said first andsecond images.

The 15th mode of the present invention is characterized in that an imagequality evaluation method comprising: calculating a difference betweendata representing a feature of a pixel group comprised of at least onepixel making up a first image, and data representing a feature of apixel group comprised of at least one pixel making up a second image;deciding a focused area in the image having a predetermined featureusing at least one of said first and second images, and calculating adegree of focused area indicating the degree of being said focused area;applying weighting to the difference in feature for a pixel groupfalling within said focused area based on said degree of focused area;and calculating an image quality value for said first image based onsaid weighted difference.

The 16th mode of the present invention, in the above-mentioned modes, ischaracterized in that decision of said focused area is performed on apixel group-by-pixel group basis.

The 17th mode of the present invention, in the above-mentioned modes, ischaracterized in that the image quality evaluation method comprisingdeciding whether a pixel is a pixel of interest for said focused areabased on at least a pixel value of said pixel making up said first orsecond image; and deciding a focused area comprised of at least one ormore pixels based on said pixel of interest.

The 18th mode of the present invention, in the above-mentioned modes, ischaracterized in that the image quality evaluation method comprisingdeciding a pixel as a pixel of interest in a case that the pixel valueof said pixel falls within a specific range in a color space.

The 19th mode of the present invention, in the above-mentioned modes, ischaracterized in that said specific range in said color space is apredetermined range defined by a YCbCr color space represented by aluminance value and a color difference value.

The 20th mode of the present invention, in the above-mentioned modes, ischaracterized in that said specific range in said color space is a rangewith a value Y indicating the luminance ranging 48≦y≦224, a value Cbindicating the blue difference ranging 104<Cb<125, and a value Crindicating the red difference ranging 135<Cr<171.

The 21st mode of the present invention, in the above-mentioned modes, ischaracterized in that said specific range in said color space is apredetermined range defined in an RGB color space represented by RGBvalues indicating three primary colors, red, blue and green.

The 22nd mode of the present invention, in the above-mentioned modes, ischaracterized in that the image quality evaluation method comprising:calculating a degree of focused area for a pixel group of pixels decidedto be said pixel of interest as one and that for a pixel group of pixelsdecided not to be said pixel of interest as zero.

The 23rd mode of the present invention, in the above-mentioned modes, ischaracterized in that the image quality evaluation method comprising:accumulating the number of pixels decided to be said pixel of interestamong pixels within a pixel group; and calculating a degree of focusedarea for said pixel group based on a result of said accumulation.

The 24th mode of the present invention, in the above-mentioned modes, ischaracterized in that the image quality evaluation method comprising:accumulating the number of pixels decided to be a pixel of interestamong pixels within a pixel group and the number of pixels decided to bea pixel of interest among pixels near said pixel group; and calculatinga degree of focused area for said pixel group based on a result of theaccumulation.

The 25th mode of the present invention, in the above-mentioned modes, ischaracterized in that said data representing a feature for the firstimage and that representing a feature for the second image areinformation on any one of a luminance value, a color difference valueand an RGB value, or a combination thereof, for at least part of pixelsmaking up said first and second images.

The 26th mode of the present invention, in the above-mentioned modes, ischaracterized in that said data representing a feature for the firstimage and that representing a feature for the second image are anaverage of information on any one of a luminance value, a colordifference value and an RGB value, or a combination thereof, for pixelscontained in at least part of pixel groups making up said first andsecond images.

The 27th mode of the present invention, in the above-mentioned modes, ischaracterized in that said data representing a feature for the firstimage and that representing a feature for the second image are anaverage of absolute differences within an image group between an averageof information on any one of a luminance value, a color difference valueand an RGB value, or a combination thereof, and said information foreach pixel within said pixel group, for pixels contained in at leastpart of pixel groups making up said first and second images.

The 28th mode of the present invention, in the above-mentioned modes, ischaracterized in that said data representing a feature for the firstimage and that representing a feature for the second image are avariance of information on any one of a luminance value, a colordifference value and an RGB value, or a combination thereof, for pixelscontained in at least part of pixel groups making up said first andsecond images.

The 29th mode of the present invention is characterized in that aprogram causing an information processing apparatus to execute theprocessing of: calculating a difference between data representing afeature of a pixel group comprised of at least one pixel making up afirst image, and data representing a feature of a pixel group comprisedof at least one pixel making up a second image; deciding a focused areain the image having a predetermined feature using at least one of saidfirst and second images, and calculating a degree of focused areaindicating the degree of being said focused area; applying weighting tothe difference in feature for a pixel group falling within said focusedarea based on said degree of focused area; and calculating an imagequality value for said first image based on said weighted difference.

Above, although the present invention has been particularly describedwith reference to the preferred embodiments and modes thereof, it shouldbe readily apparent to those of ordinary skill in the art that thepresent invention is not always limited to the above-mentionedembodiment and modes, and changes and modifications in the form anddetails may be made without departing from the sprit and scope of theinvention.

This application is based upon and claims the benefit of priority fromJapanese patent application No. 2008-118349, filed on Apr. 30, 2008, thedisclosure of which is incorporated herein in its entirety by reference.

1. An image quality evaluation system comprising: a differencecalculator that calculates a difference between data representing afeature of a pixel group comprised of at least one pixel making up afirst image, and data representing a feature of a pixel group comprisedof at least one pixel making up a second image; a degree-of-focused-areacalculator that decides a focused area in the image having apredetermined feature using at least one of said first and secondimages, and calculates a degree of focused area indicating the degree ofbeing said focused area; a difference weighting section that appliesweighting to the difference in feature for a pixel group falling withinsaid focused area based on said degree of focused area; and an imagequality value calculator that calculates an image quality value for saidfirst image based on the difference weighted by said differenceweighting section.
 2. An image quality evaluation system according toclaim 1, wherein said degree-of-focused-area calculator performsdecision of a focused area on a pixel group-by-pixel group basis.
 3. Animage quality evaluation system according to claim 1, wherein saiddegree-of-focused-area calculator decides whether a pixel is a pixel ofinterest for said focused area based on at least a pixel value of saidpixel making up said first or second image, and decides a focused areacomprised of at least one or more pixels based on said pixel ofinterest.
 4. An image quality evaluation system according to claim 3,wherein said degree-of-focused-area calculator decides a pixel as apixel of interest in a case that a pixel value of said pixel fallswithin a specific range in a color space.
 5. An image quality evaluationsystem according to claim 4, wherein said specific range in said colorspace is a predetermined range defined by a YCbCr color spacerepresented by a luminance value and a color difference value.
 6. Animage quality evaluation system according to claim 4, wherein saidspecific range in said color space is a range with a value Y indicatingthe luminance ranging 48≦y≦224, a value Cb indicating the bluedifference ranging 104<Cb<125, and a value Cr indicating the reddifference ranging 135<Cr<171.
 7. An image quality evaluation systemaccording to claim 4, wherein said specific range in said color space isa predetermined range defined in an RGB color space represented by RGBvalues indicating three primary colors, red, blue and green.
 8. An imagequality evaluation system according to claim 3, wherein saiddegree-of-focused-area calculator calculates a degree of focused areafor a pixel group of pixels decided to be said pixel of interest as oneand that for a pixel group of pixels decided not to be said pixel ofinterest as zero.
 9. An image quality evaluation system according toclaim 3, wherein said degree-of-focused-area calculator accumulates thenumber of pixels decided to be said pixel of interest among pixelswithin said pixel group, and calculates a degree of focused area forsaid pixel group based on a result of said accumulation.
 10. An imagequality evaluation system according to claim 3, wherein saiddegree-of-focused-area calculator accumulates the number of pixelsdecided to be a pixel of interest among pixels within said pixel groupand the number of pixels decided to be a pixel of interest among pixelsnear said pixel group, and calculates a degree of focused area for saidpixel group based on a result of the accumulation.
 11. An image qualityevaluation system according to claim 1, wherein said data representing afeature for the first image and that representing a feature for thesecond image are information on any one of a luminance value, a colordifference value and an RGB value, or a combination thereof, for atleast part of pixels making up said first and second images.
 12. Animage quality evaluation system according to claim 1, wherein said datarepresenting a feature for the first image and that representing afeature for the second image are an average of information on any one ofa luminance value, a color difference value and an RGB value, or acombination thereof, for pixels contained in at least part of pixelgroups making up said first and second images.
 13. An image qualityevaluation system according to claim 1, wherein said data representing afeature for the first image and that representing a feature for thesecond image are an average of absolute differences within an imagegroup between an average of information on any one of a luminance value,a color difference value and an RGB value, or a combination thereof, andsaid information for each pixel within said pixel group, for pixelscontained in at least part of pixel groups making up said first andsecond images.
 14. An image quality evaluation system according to claim1, wherein said data representing a feature for the first image and thatrepresenting a feature for the second image are a variance ofinformation on any one of a luminance value, a color difference valueand an RGB value, or a combination thereof, for pixels contained in atleast part of pixel groups making up said first and second images. 15.An image quality evaluation method comprising: calculating a differencebetween data representing a feature of a pixel group comprised of atleast one pixel making up a first image, and data representing a featureof a pixel group comprised of at least one pixel making up a secondimage; deciding a focused area in the image having a predeterminedfeature using at least one of said first and second images, andcalculating a degree of focused area indicating the degree of being saidfocused area; applying weighting to the difference in feature for apixel group falling within said focused area based on said degree offocused area; and calculating an image quality value for said firstimage based on said weighted difference.
 16. An image quality evaluationmethod according to claim 15, wherein decision of said focused area isperformed on a pixel group-by-pixel group basis.
 17. An image qualityevaluation method according to claim 15 or 16, comprising: decidingwhether a pixel is a pixel of interest for said focused area based on atleast a pixel value of said pixel making up said first or second image;and deciding a focused area comprised of at least one or more pixelsbased on said pixel of interest.
 18. An image quality evaluation methodaccording to claim 17, comprising deciding a pixel as a pixel ofinterest in a case that the pixel value of said pixel falls within aspecific range in a color space.
 19. An image quality evaluation methodaccording to claim 18, wherein said specific range in said color spaceis a predetermined range defined by a YCbCr color space represented by aluminance value and a color difference value.
 20. An image qualityevaluation method according to claim 18, wherein said specific range insaid color space is a range with a value Y indicating the luminanceranging 48≦y≦224, a value Cb indicating the blue difference ranging104<Cb<125, and a value Cr indicating the red difference ranging135<Cr<171.
 21. An image quality evaluation method according to claim18, wherein said specific range in said color space is a predeterminedrange defined in an RGB color space represented by RGB values indicatingthree primary colors, red, blue and green.
 22. An image qualityevaluation method according to claim 17, comprising calculating a degreeof focused area for a pixel group of pixels decided to be said pixel ofinterest as one and that for a pixel group of pixels decided not to besaid pixel of interest as zero.
 23. An image quality evaluation methodaccording to claim 17, comprising: accumulating the number of pixelsdecided to be said pixel of interest among pixels within a pixel group;and calculating a degree of focused area for said pixel group based on aresult of said accumulation.
 24. An image quality evaluation methodaccording to claim 17, comprising: accumulating the number of pixelsdecided to be a pixel of interest among pixels within a pixel group andthe number of pixels decided to be a pixel of interest among pixels nearsaid pixel group; and calculating a degree of focused area for saidpixel group based on a result of the accumulation.
 25. An image qualityevaluation method according to claim 15, wherein said data representinga feature for the first image and that representing a feature for thesecond image are information on any one of a luminance value, a colordifference value and an RGB value, or a combination thereof, for atleast part of pixels making up said first and second images.
 26. Animage quality evaluation method according to claim 15, wherein said datarepresenting a feature for the first image and that representing afeature for the second image are an average of information on any one ofa luminance value, a color difference value and an RGB value, or acombination thereof, for pixels contained in at least part of pixelgroups making up said first and second images.
 27. An image qualityevaluation method according to claim 15, wherein said data representinga feature for the first image and that representing a feature for thesecond image are an average of absolute differences within an imagegroup between an average of information on any one of a luminance value,a color difference value and an RGB value, or a combination thereof, andsaid information for each pixel within said pixel group, for pixelscontained in at least part of pixel groups making up said first andsecond images.
 28. An image quality evaluation method according to claim15, wherein said data representing a feature for the first image andthat representing a feature for the second image are a variance ofinformation on any one of a luminance value, a color difference valueand an RGB value, or a combination thereof, for pixels contained in atleast part of pixel groups making up said first and second images.
 29. Anon-transitory computer readable storage medium storing a programcausing an information processing apparatus to execute the processingof: calculating a difference between data representing a feature of apixel group comprised of at least one pixel making up a first image, anddata representing a feature of a pixel group comprised of at least onepixel making up a second image; deciding a focused area in the imagehaving a predetermined feature using at least one of said first andsecond images, and calculating a degree of focused area indicating thedegree of being said focused area; applying weighting to the differencein feature for a pixel group falling within said focused area based onsaid degree of focused area; and calculating an image quality value forsaid first image based on said weighted difference.
 30. An image qualityevaluation method comprising: calculating a difference between datarepresenting a feature of a pixel group comprised of at least one pixelmaking up a first image, and data representing a feature of a pixelgroup comprised of at least one pixel making up a second image; decidinga focused area in the image having a predetermined feature on a pixelgroup-by-pixel group basis using at least one of said first and secondimages; and applying weighting to the difference in feature for a pixelgroup falling within said decided focused area.
 31. An image qualityevaluation method according to claim 30, wherein the decision of saidfocused area is, based on at least a pixel value of the pixel making upsaid first or second image, deciding whether or not the pixel value ofsaid pixel falls within a specific range in a color space, and decidingsaid pixel group as a focused area based on the pixel which has beendecided to be fallen within said specific range.
 32. An image qualityevaluation method according to claim 31, wherein the specific range insaid color space is a predetermined range defined by a YCbCr color spacerepresented by a luminance value and a color difference value.
 33. Animage quality evaluation method according to claim 31, wherein thespecific range in said color space is a range with a value Y indicatingluminance ranging 48≦y≦224, a value Cb indicating a blue differenceranging 104<Cb<125, and a value Cr indicating a red difference ranging135<Cr<171.
 34. An image quality evaluation method according to claim31, wherein the decision of said focused area is accumulating the numberof pixels which have been decided to be fallen within the specific rangein said color space among pixels within said pixel group and within apixel group near said pixel group; and in a case that said number of thepixels is greater than or equal to a predetermined threshold, decidingsaid pixel group as a focused area.